Lecture 11 Segmentation - Stanford University

Lecture 11 Segmentation - Stanford University

Lecture 11 Segmentation and Grouping Gary Bradski Sebastian Thrun * http://robots.stanford.edu/cs223b/index.html 1 * Pictures from Mean Shift: A Robust Approach toward Feature Space Analysis, by D. Comaniciu and P. Meer http://www.caip.rutgers.edu/~comanici/MSPAMI/msPamiResults.htm Outline Segmentation Intro What and why Biological Segmentation: By learning the background

By energy minimization Normalized Cuts By clustering Mean Shift (perhaps the best technique to date) By fitting optional, but projects doing SFM should read. Reading source: Forsyth Chapters in segmentation, available (at least this term) http://www.cs.berkeley.edu/~daf/new-seg.pdf 2 Intro: Segmentation and Grouping What: Segmentation breaks an image into groups over space and/or time Why: Motivation: not for recognition for compression Relationship of

sequence/set of tokens Always for a goal or application Currently, no real theory Tokens are The things that are grouped (pixels, points, surface elements, etc., etc.) top down segmentation tokens grouped because they lie on the same object bottom up segmentation tokens belong together because of some local affinity measure

Bottom up/Top Dowon need not be mutually exclusive 3 Biological: Segmentation in Humans 4 Biological: For humans at least, Gestalt psychology identifies several properties that result In grouping/segmentation: 5 Biological: For humans at least, Gestalt psychology identifies several properties that result In grouping/segmentation:

6 Consequence: Groupings by Invisible Completions Stressing the invisible groupings: 7 * Images from Steve Lehars Gestalt papers: http://cns-alumni.bu.edu/pub/slehar/Lehar.html Consequence: Groupings by Invisible Completions 8 * Images from Steve Lehars Gestalt papers: http://cns-alumni.bu.edu/pub/slehar/Lehar.html Consequence: Groupings by Invisible Completions 9

* Images from Steve Lehars Gestalt papers: http://cns-alumni.bu.edu/pub/slehar/Lehar.html Here, the 3D nature of grouping is apparent: Why do these tokens belong together? Corners and creases in 3D, length is interpreted differently: In Out The (in) line at the far end of corridor must be longer than the (out) near line if they measure to be the same size 10 And the famous invisible dog eating under a tree:

11 Background Subtraction 12 Background Subtraction 1. Learn model of the background 2. Take absolute difference with current frame 3. Pixels greater than a threshold are candidate foreground

Use morphological open operation to clean up point noise. Traverse the image and use flood fill to measure size of candidate regions. 4. 5. By statistics (); mixture of Gaussians; Adaptive filter, etc Assign as foreground those regions bigger than a set value. Zero out regions that are too small. Track 3 temporal modes: (1) Quick regional changes are foreground (people, moving cars); (2) Changes that stopped a medium time ago are candidate background (chairs that got moved etc); (3) Long term statistically stable regions are background.

13 Background Subtraction Example 14 Background Subtraction Principles At ICCV 1999, MS Research presented a study, Wallflower: Principles and Practice of Background Maintenance, by Kentaro Toyama, John Krumm, Barry Brumitt, Brian Meyers. This paper compared many different background subtraction techniques and came up with some principles: P1: P2: P3: P4: P5: 15

16 From the Wallflower Paper Background Techniques Compared Segmentation by Energy Minimization: Graph Cuts 17 Graph theoretic clustering Represent tokens (which are associated with each pixel) using a weighted graph. affinity matrix (pi same as pj => affinity of 1) Cut up this graph to get subgraphs with strong interior links and weaker exterior links Application to vision originated with Prof. Malik at Berkeley

18 Graphs Representations a c d b e 0 1 0 0 1 1 0 0

0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 1 0

Adjacency Matrix: W 19 * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Weighted Graphs and Their Representations a b c e 6 d 0 1

3 1 0 4 2 3 4 0 6 7 6 0

1 2 7 1 0 Weight Matrix: W 20 * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Minimum Cut A cut of a graph G is the set of edges S such that removal of S from G disconnects G. Minimum cut is the cut of minimum weight, where weight of cut is given

as w A, B xA, yB w x, y 21 * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Minimum Cut and Clustering 22 * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Image Segmentation & Minimum Cut Pixel Neighborhood Image Pixels w

Similarity Measure Minimum Cut 23 * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Minimum Cut There can be more than one minimum cut in a given graph All minimum cuts of a graph can be found in polynomial time1. H. Nagamochi, K. Nishimura and T. Ibaraki, Computing all small cuts in an undirected network. SIAM J. Discrete Math. 10 (1997) 469-481. 1 24

* From Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Finding the Minimal Cuts: Spectral Clustering Overview Data Similarities Block-Detection 25 * Slides from Dan Klein, Sep Kamvar, Chris Manning, Natural Language Group Stanford University Eigenvectors and Blocks Block matrices have block eigenvectors: 1 = 2 2 = 2 1

1 0 0 .71 0 1 1 0 0 .71

0 0 0 1 1 0 .71 0 0 1 1

0 .71 eigensolver 3= 0 4 = 0 Near-block matrices have near-block eigenvectors: [Ng et al., NIPS 02] 1= 2.02 2= 2.02 1 1

.2 0 .71 0 1 1 0 -.2 .69 -.14 .2

0 1 1 .14 .69 0 -.2 1 1 0

.71 eigensolver 3= -0.02 4= -0.02 26 * Slides from Dan Klein, Sep Kamvar, Chris Manning, Natural Language Group Stanford University Spectral Space Can put items into blocks by eigenvectors: 1 1 .2 0

.71 0 1 1 0 -.2 .69 -.14 .2 0 1

1 .14 .69 0 -.2 1 1 0 .71 e1

e2 Clusters clear regardless of row ordering: 1 .2 1 0 .71 0 .2 1 0

1 .14 .69 1 0 1 -.2 .69 -.14 0 1

-.2 1 0 .71 e1 e2 e1 e2 e1 e2

27 * Slides from Dan Klein, Sep Kamvar, Chris Manning, Natural Language Group Stanford University The Spectral Advantage The key advantage of spectral clustering is the spectral space representation: 28 * Slides from Dan Klein, Sep Kamvar, Chris Manning, Natural Language Group Stanford University Clustering and Classification Once our data is in spectral space: Clustering Classification 29 * Slides from Dan Klein, Sep Kamvar, Chris Manning, Natural Language Group Stanford University Measuring Affinity

Intensity 1 2 aff x, yexp I x I y 2 2 i Distance

2 aff x, yexp 1 2 x y 2 d Texture 2 aff x, yexp 1 2 cx cy 2 t

30 * From Marc Pollefeys COMP 256 2003 Scale affects affinity 31 * From Marc Pollefeys COMP 256 2003 32 * From Marc Pollefeys COMP 256 2003 Drawbacks of Minimum Cut Weight of cut is directly proportional to the number of edges in the cut. Cuts with lesser weight than the ideal cut

Ideal Cut 33 * Slide from Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Normalized Cuts 1 Normalized cut is defined as N cut A, B w A, B x A, yV w x, y

w A, B zB , yV w z, y Ncut(A,B) is the measure of dissimilarity of sets A and B. Minimizing Ncut(A,B) maximizes a measure of similarity within the sets A and B J. Shi and J. Malik, Normalized Cuts & Image Segmentation, IEEE Trans. of PAMI, 34 Aug 2000. 1

* Slide from Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Finding Minimum Normalized-Cut Finding the Minimum Normalized-Cut is NP-Hard. Polynomial Approximations are generally used for segmentation 35 * Slide from Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Finding Minimum Normalized-Cut W N N symmetric matrix, where e W i, j Fi F j F2 e

0 X i X j X2 if j N i otherwise Fi F j Image feature similarity X i X j Spatial Proximity D N N diagonal matrix, where D i, i W i, j j 36 * From Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Finding Minimum Normalized-Cut It can be shown that min N cut

yT D Wy min y y T Dy such that y i 1, b , 0 b 1, and y T D1 0 If y is allowed to take real values then the minimization can be done by solving the generalized eigenvalue system D W y Dy 37 * Slide from Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Algorithm

Compute matrices W & D Solve D W y Dy for eigen vectors with the smallest eigen values Use the eigen vector with second smallest eigen value to bipartition the graph Recursively partition the segmented parts if necessary. 38 * Slide from Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Figure from Image and video segmentation: the normalised cut framework, by Shi and Malik, 1998 39 * Slide from Khurram Hassan-Shafique CAP5415 Computer Vision 2003 F igure from Normalized cuts and image segmentation, Shi and Malik, 2000 40 * Slide from Khurram Hassan-Shafique CAP5415 Computer Vision 2003

Drawbacks of Minimum Normalized Cut Huge Storage Requirement and time complexity Bias towards partitioning into equal segments Have problems with textured backgrounds 41 * Slide from Khurram Hassan-Shafique CAP5415 Computer Vision 2003 Segmentation by Clustering 42 Segmentation as clustering Cluster together (pixels, tokens, etc.) that belong together Agglomerative clustering attach closest to cluster it is closest to

repeat Divisive clustering Point-Cluster distance single-link clustering complete-link clustering group-average clustering Dendrograms yield a picture of output as clustering process continues split cluster along best boundary repeat 43 * From Marc Pollefeys COMP 256 2003 Simple clustering algorithms

44 * From Marc Pollefeys COMP 256 2003 45 * From Marc Pollefeys COMP 256 2003 Mean Shift Segmentation Perhaps the best technique to date http://www.caip.rutgers.edu/~comanici/MSPAMI/msPamiResults.html 46 Mean Shift Algorithm Mean Shift Algorithm 1. Choose a search window size. 2. Choose the initial location of the search window. 3. Compute the mean location (centroid of the data) in the search window. 4. Center the search window at the mean location computed in Step 3. 5. Repeat Steps 3 and 4 until convergence.

The mean shift algorithm seeks the mode or point of highest density of a data distribution: 47 Mean Shift Segmentation Mean Shift Setmentation Algorithm 1. Convert the image into tokens (via color, gradients, texture measures etc). 2. Choose initial search window locations uniformly in the data. 3. Compute the mean shift window location for each initial position. 4. Merge windows that end up on the same peak or mode. 5. The data these merged windows traversed are clustered together. *Image From: Dorin Comaniciu and Peter Meer, Distribution Free Decomposition of Multivariate Data, Pattern Analysis & Applications (1999)2:2230 48 Mean Shift Segmentation Extension Is scale (search window size) sensitive. Solution, use all scales:

Gary Bradskis internally published agglomerative clustering extension: Mean shift dendrograms 1. Place a tiny mean shift window over each data point 2. Grow the window and mean shift it 3. Track windows that merge along with the data they transversed 4. Until everything is merged into one cluster Best 4 clusters: Best 2 clusters: Advantage over agglomerative clustering: Highly parallelizable 49 Mean Shift Segmentation Results: http://www.caip.rutgers.edu/~comanici/MSPAMI/msPamiResults.html 50 K-Means

Choose a fixed number of clusters Choose cluster centers and point-cluster allocations to minimize error cant do this by search, because there are too many possible allocations. Algorithm

fix cluster centers; allocate points to closest cluster fix allocation; compute best cluster centers x could be any set of features for which we can compute a distance (careful about scaling) 2 x j i

iclusters jelements of i'th cluster 51 * From Marc Pollefeys COMP 256 2003 K-Means 52 * From Marc Pollefeys COMP 256 2003 Image Segmentation by K-Means

Select a value of K Select a feature vector for every pixel (color, texture, position, or combination of these etc.) Define a similarity measure between feature vectors (Usually Euclidean Distance). Apply K-Means Algorithm. Apply Connected Components Algorithm. Merge any components of size less than some threshold to an adjacent component that is most similar to it. 53 * From Marc Pollefeys COMP 256 2003 Results of K-Means Clustering: Image Clusters on intensity

Clusters on color K-means clustering using intensity alone and color alone 54 * From Marc Pollefeys COMP 256 2003 Optional Section: Fitting with RANSAC (RANdom SAmple Consensus) Who should read? Everyone doing a project that requires: Structure from motion or finding a Fundamental or Essential matrix 55 RANSAC Choose a small subset uniformly at random Fit to that

Anything that is close to result is signal; all others are noise Refit Do this many times and choose the best Issues How many times? Often enough that we are likely to have a good line How big a subset? Smallest possible What does close mean? Depends on the problem What is a good line? One where the number of nearby points is so big it is

unlikely to be all outliers 56 * From Marc Pollefeys COMP 256 2003 57 * From Marc Pollefeys COMP 256 2003 Distance threshold Choose t so probability for inlier is (e.g. 0.95) Often empirically 2 Zero-mean Gaussian noise then d follows m2 distribution with m=codimension of model (dimension+codimension=dimension space) Codimensio n Model

t2 1 line,F 3.842 2 H,P 5.992 3 T 7.812 58 * From Marc Pollefeys COMP 256 2003

How many samples? Choose N so that, with probability p, at least one random sample is free from outliers. e.g. p=0.99 s N 1 1 e 1 p N log1 p / log 1 1 e s proportion of outliers e s

2 3 4 5 6 7 8 5% 2 3 3 4 4 4 5 10% 20% 25% 30% 40% 50% 3 5 6

7 11 17 4 7 9 11 19 35 5 9 13 17 34 72 6 12 17 26 57 146 7

16 24 37 97 293 8 20 33 54 163 588 9 26 44 78 272 1177 59 * From Marc Pollefeys COMP 256 2003 Acceptable consensus set? Typically, terminate when inlier ratio reaches expected ratio of inliers T 1 e n

60 * From Marc Pollefeys COMP 256 2003 Adaptively determining the number of samples e is often unknown a priori, so pick worst case, e.g. 50%, and adapt if more inliers are found, e.g. 80% would yield e=0.2 N=, sample_count =0 While N >sample_count repeat Choose a sample and count the number of inliers Set e=1-(number of inliers)/(total number of points) Recompute N from e Increment the sample_count by 1 Terminate

N log1 p / log1 1 e s 61 * From Marc Pollefeys COMP 256 2003 RANSAC for Fundamental Matrix Step 1. Extract features Step 2. Compute a set of potential matches Step 3. do Step 3.1 select minimal sample (i.e. 7 matches) Step 3.2 compute solution(s) for F Step 3.3 determine inliers (verify hypothesis) (generate hypothesis) until (#inliers,#samples)<95%

Step 4. Compute F based on all inliers Step 5. Look for additional matches Step 6. Refine F based on all correct matches 1 (1 #inliers #sample 90% 5 # inliers 7 # samples ) # matches 80 %

70% 13 35 60% 50% 62 106 382 * From Marc Pollefeys COMP 256 2003 Randomized RANSAC for Fundamental Matrix Step 1. Extract features Step 2. Compute a set of potential matches Step 3. do

Step 3.1 select minimal sample (i.e. 7 matches) Step 3.2 compute solution(s) for F Step 3.3 Randomize verification 3.3.1 verify if inlier while hypothesis is still promising while (#inliers,#samples)<95% (generate hypothesis) (verify hypothesis) Step 4. Compute F based on all inliers Step 5. Look for additional matches Step 6. Refine F based on all correct matches 63

* From Marc Pollefeys COMP 256 2003 Example: robust computation from H&Z Interest points (500/image) #in (640x480) 1-e adapt. N 6 2% 20M 10

3% 2.5M 44 16% 6,922 58 21% 2,291 73 26% 911

151 56% 43 Putative correspondences (268) (Best match,SSD<20,320) Outliers (117) (t=1.25 pixel; 43 iterations) Inliers (151) Final inliers (262) * From Marc Pollefeys COMP 256 2003 (2 MLE-inlier cycles; d=0.23dd=0.19;

IterLev-Mar=10) 64 More on robust estimation LMedS, an alternative to RANSAC (minimize Median residual in stead of maximizing inlier count) Enhancements to RANSAC Randomized RANSAC Sample good matches more frequently RANSAC is also somewhat robust to bugs, sometimes it just takes a bit longer 65 * From Marc Pollefeys COMP 256 2003

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